Particle and Particle Agglomerate Size Monitoring by Scanning Probe Microscope
Source abstract
Abstract
In the present study, the use of a scanning probe microscope is described for monitoring the sizes of nanoparticles. Monitoring is the process of acquiring and analysing the set of overlapping images. The main analysis steps are image segmentation, determination of nanoparticles allocation and their sizes, determination of the overlap of images with one another, and exclusion of repeating measurements for the formation of the correct particle-size sampling. The thorough examination of commercial scanning probe microscopes, software, and image processing libraries showed that their capabilities are limited for image segmentation and determination of sizes in complex structured images. A method based on the surface curvature computation is proposed for the image segmentation (allocation of particles) and determination of particle sizes. The curvature is estimated using the surface area approximation with respect to the circumference. It is proposed to use sample displacement sensors as an aid for image stitching.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Force Microscopy Techniques and Applications · Surface Roughness and Optical Measurements · Image Processing Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Udmurt Federal Research Centre of the Ural Branch of the Russian Academy of Sciences, Institute of Mechanics, 426067 Izhevsk, Russia
Department of Technical Systems Design and Monitoring, Faculty of Manufacturing Technologies with a Seat in Presov, Technical University of Kosice, Sturova 31, 080 01 Presov, Slovakia
Funding
Slovak Ministry of Education · Slovak Research and Development Agency
References (46)
Show source-provided references
1. Merkus, H.G. (2009). Particle Size Measurements, Springer.
2. Tekade, R.K. (2019). Importance of physicochemical characterization of nanoparticles in pharmaceutical product development. Advances in Pharmaceutical Product Development and Research, Basic Fundamentals of Drug Delivery, Academic Press.
3. Particle size analysis by laser diffraction: ISO 13320, standard operating procedures, and Mie theory
4. Particle size distributions by laser diffraction: Sensitivity of granular matter strength to analytical operating procedures10.5194/se-1-25-2010 ↗
5. Revisiting the method of cumulants for the analysis of dynamic Light-Scattering data10.1364/AO.40.004087 ↗
6. Dynamic Light Scattering for the Characterization of Polydisperse Fractal Systems: II. Relation between Structure and DLS Results10.1002/ppsc.200700005 ↗
7. Effects of impurities on particle sizing by acoustic attenuation spectroscopy10.1016/j.powtec.2010.02.033 ↗
8. Applications for emulsions and other soft particles10.1016/S1383-7303(10)23010-7 ↗
9. Nanoparticle film deposition using a simple and fast centrifuge sedimentation method10.1007/s13204-014-0338-x ↗
10. Image Analysis for Particle Size Distribution
11. Mulligan, S., Ojeda, J., Kakonyi, G., Thornton, S., Moharamzadeh, K., and Martin, N. (2021). Characterisation of Microparticle Waste from Dental Resin-Based Composites. Materials, 14.10.3390/ma14164440 ↗
12. Origin and characterization of clay deposits in the DupiTila Formation of the Bengal Basin, Bangladesh
13. Measurement of the size of spherical nanoparticles by means of atomic force microscopy10.1088/0957-0233/22/6/065101 ↗
14. Size Measurement of Nanoparticles Using Atomic Force Microscopy10.1007/978-1-60327-198-1_7 ↗
15. Size measurements of standard nanoparticles using metrological atomic force microscope and evaluation of their uncertainties10.1016/j.precisioneng.2017.11.013 ↗
16. Determination of nanoparticle sizes by X-ray diffraction10.1134/S1061933X12060051 ↗
17. Linsinger, T., Roebben, G., Gilliland, D., Calzolai, L., Rossi, F., Gibson, N., and Klein, C. (2012). Requirements on Measurements for the Implementation of the European Commission Definition of the Term ‘Nanomaterial’, Publications Office of the European Union.
18. Sun, C., Beare, R., Hilsenstein, V., and Jackway, P. (2005, January 6). Mosaicing of microscope images. Proceedings of the Digital Imaging Computing: Techniques and Applications, Cairns, Australia.
19. ISO 13322-1:2014 (2014). Particle Size Analysis—Image Analysis Methods—Part 1: Static Image Analysis Methods, International Organization for Standardization. [2nd ed.].
20. ISO 13322-1:2016 (2016). Particle Size Analysis—Image Analysis Methods—Part 2: Dynamic Image Analysis Methods, International Organization for Standardization. [2nd ed.].
21. (2021, September 07). OpenCV. Available online: https://opencv.org/.
22. (2021, September 07). Feature Matching with FLANN. Available online: www.docs.opencv.org/3.4/d5/d6f/tutorial_feature_flann_matcher.html.
23. (2021, February 11). SPM NEXT II. Available online: https://www.ntmdt-si.com/products/automated-afm/next-ii.
24. Parallel nanoimaging and nanolithography using a heated microcantilever array10.1088/0957-4484/25/1/014001 ↗
25. Fast Specimen Boundary Tracking and Local Imaging with Scanning Probe Microscopy
26. Beucher, S., and Lantuéj, C. (1979, January 17–21). Use of whatersheds in contuor detection. Proceedings of the Workshop on Image Processing, Real-Time Edge and Motion Detection, Rennes, France. Available online: http://cmm.ensmp.fr/~beucher/publi/watershed.pdf.
27. A Threshold Selection Method from Gray-Level Histograms10.1109/TSMC.1979.4310076 ↗
28. Color image segmentation based on adaptive local thresholds10.1016/j.imavis.2004.05.011 ↗
29. Segmentation of Natural Images Using an Improved Thresholding-Based Technique10.1016/j.proeng.2012.07.266 ↗
30. Segmentation of scanning tunneling microscopy images using variational methods and empirical wavelets10.1007/s10044-019-00824-0 ↗
31. Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5–9). U-Net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.10.1007/978-3-319-24574-4_28 ↗
32. Reference marks application for surface coordinate binding in scanning probe microscopy10.18287/2412-6179-CO-641 ↗
33. (2021, February 11). Scanning Probe Microscope AFM5500M. Available online: https://www.hitachi-hightech.com/global/product_detail/?pn=em-afm5500m).
34. (2021, February 11). Atomic Force Microscope Dimension Icon. Available online: https://www.bruker.com/en/products-and-solutions/microscopes/materials-afm/dimension-icon-afm.html.
35. Comparison of binary feature points descriptors of images under distortion conditions10.18287/2412-6179-2019-43-3-434-445 ↗
36. Restoration of Scanning Tunnelling Microscope data, blurred by limited resolution, and hampered by 1/f-like noise10.1016/0039-6028(87)90162-2 ↗
37. ORB: An efficient alternative to SIFT or SURF
38. Herbert, B., Ess, A., Tuytelaars, T., and Van Gool, L. (2006). SURF: Speeded up robust features. Computer vision and image understanding. Lecture Notes in Computer Science, Springer.
39. (2021, February 11). Topostitch. Available online: https://www.topostitch.com/.
40. Real-time curvature defect detection on outer surfaces using best-fit polynomial interpolation10.3390/s121114774 ↗
41. Chord-to-point distance accumulation and planar curvature: A new approach to discrete curvature10.1016/S0167-8655(01)00063-0 ↗
42. Measurement of the length of objects on scanning probe microscope images using curvature detectors10.1007/s11018-021-01890-9 ↗
43. Chen, C., and Klette, R. (1997, January 22). An image stitcher and its application in panoramic movie making. Proceedings of the DICTA’97, Auckland, New Zealand.
44. Surface images creation at the multiscan mode of the scanning tunnel microscope
45. Software-Hardware Systems for Measurement of Sample Displacements in Probe Microscopes10.1007/s11018-019-01537-w ↗
46. (2022, February 17). NT-MDT. Spectrum Instruments. Scan Gallery. Available online: https://www.ntmdt-si.ru/resources/scan-gallery.